560 Matching Annotations
  1. Last 7 days
    1. communication partners

      super interesting that Luhmann referred to his zettelkasten as a communication partner explicitly himself.

      also interesting given AI models are easier to train now with several models already open sourced which allows actual interaction with your notes! would love to see where it goes.

    1. I would submit that were we to find ways of engineering our quote-unquote ape brains um what would all what what would be very likely to happen would not be um 00:35:57 some some sort of putative human better equipped to deal with the complex world that we have it would instead be something more like um a cartoon very much very very much a 00:36:10 repeat of what we've had with the pill
      • Comment
        • Mary echos Ronald Wright's progress traps
    2. with their new different and perhaps bigger brains the AIS of the future may prove themselves to be better adapted to 00:19:05 life in this transhuman world that we're in now
      • comment
        • Is this not a category error in classifying inert technology as life?
        • When does an abiotic human cultural artefact become a living form?
    1. Deep Learning (DL) A Technique for Implementing Machine LearningSubfield of ML that uses specialized techniques involving multi-layer (2+) artificial neural networksLayering allows cascaded learning and abstraction levels (e.g. line -> shape -> object -> scene)Computationally intensive enabled by clouds, GPUs, and specialized HW such as FPGAs, TPUs, etc.

      [29] AI - Deep Learning

    1. The object of the present volume is to point out the effects and the advantages which arise from the use of tools and machines ;—to endeavour to classify their modes of action ;—and to trace both the causes and the consequences of applying machinery to supersede the skill and power of the human arm.

      [28] AI - precedents...

    1. Epidemiologist Michael Abramson, who led the research, found that the participants who texted more often tended to work faster but score lower on the tests.

      [21] AI - Skills Erosion

    1. An AI model taught to view racist language as normal is obviously bad. The researchers, though, point out a couple of more subtle problems. One is that shifts in language play an important role in social change; the MeToo and Black Lives Matter movements, for example, have tried to establish a new anti-sexist and anti-racist vocabulary. An AI model trained on vast swaths of the internet won’t be attuned to the nuances of this vocabulary and won’t produce or interpret language in line with these new cultural norms. It will also fail to capture the language and the norms of countries and peoples that have less access to the internet and thus a smaller linguistic footprint online. The result is that AI-generated language will be homogenized, reflecting the practices of the richest countries and communities.

      [21] AI Nuances

    1. According to him, there are several goals connected to AI alignment that need to be addressed:

      [20] AI - Alignment Goals

    1. The following table lists the results that we visualized in the graphic.

      [18] AI - Increased sophistication

    1. A novel architecture that makes it possible for generativeagents to remember, retrieve, reflect, interact with otheragents, and plan through dynamically evolving circumstances.The architecture leverages the powerful prompting capabili-ties of large language models and supplements those capa-bilities to support longer-term agent coherence, the abilityto manage dynamically-evolving memory, and recursivelyproduce more generations.

      AI is turning humans to look inward for a new take on life as our identities and roles within society are being profoundly disrupted and transformed by Artificial Intelligence systems that can replicate or exhibit human-like behavior. It is also a great reminder of how complex social interactions are.

  2. May 2023
    1. Expand technical AI safety research funding

      Private sector investment in AI research under-emphasises safety and security.

      Most public investment to date has been very narrow, and the paper recommends a significant increase in public funding for technical AI safety research:

      • Alignment of system performance with intended outcomes
      • Robustness and assurance
      • Explainability of results
    2. Introduce measures to prevent and track AI model leaks

      The authors see unauthorised leakage of AI Models as a risk not just to the commercial developers but also for unauthorised use. They recommend government-mandated watermarking for AI models.

    3. Establish liability for AI-caused harm

      AI systems can perform in ways that may be unforeseen, even by their developers, and this risk is expected to grow as different AI systems become interconnected.

      There is currently no clear legal framework in any jurisdiction to assign liability for harm caused by such systems.

      The paper recommends the development of a framework for assigning liability for AI-derived harms, and asserts that this will incentivise profit-driven AI developers to use caution.

    4. Regulate organizations’ access to computational power

      Training of state-of-the-art models consumes vast amounts of computaitonal power, limiting their deployment to only the best-resourced actors.

      To prevent reckless training of high risk models the paper recommends that governments control access to large amounts of specialised compute resource subject to a risk assessment, with an extension of "know your customer" legislation.

    5. Mandate robust third-party auditing and certification for specificAI systems

      Some AI systems will be deployed in contexts that imply risks to physical, mental and/or financial health of individuals, communities or even the whole of society.

      The paper recommends that such systems should be subject to mandatory and independent audit and certification before they are deployed.

    6. Establish capable AI agencies at national level

      Article notes: * UK Office for Artificial Intelligence * EU legislation in progress for an AI Board * US pending legislation (ref Ted Lieu) to create a non-partisan AI Commission tasked with establishing a regulatory agency

      Recommends Korinek's blueprint for an AI regulatory agency:

      1. Monitor public developments in AI progress
      2. Mandate impact assessments of AI systems on various stakeholders
      3. Establish enforcement authority to act upon risks identified in impact assessments
      4. Publish generalized lessons from the impact assessments
    7. Develop standards for identifying and managing AI-generatedcontent and recommendations

      A coherent society requires a shared understanding of what is fact. AI models are capable of generating plausible-sounding but entirely wrong content.

      It is essential that the public can clearly distinguish content by human creators from synthetic content.

      Policy should therefore focus on:

      • funding for development of ways to clearly mark digital content provenance
      • laws to force disclosure of interactions with a chatbot
      • laws to require AI to be deployed in ways that are in the best interest of the user
      • laws that require 'duty of care' when AI deployed in circumstances where a human actor would have a fiduciary responsiblity
    1. Oregon State University will build a state-of-the-art artificial intelligence research center with a supercomputer and a cyberphysical playground.
    1. must have an alignment property

      It is unclear what form the "alignment property" would take, and most importantly how such a property would be evaluated especially if there's an arbitrary divide between "dangerous" and "pre-dangerous" levels of capabilities and alignment of the "dangerous" levels cannot actually be measured.

    1. Limitations

      GPT models are prone to "hallucinations", producing false "facts" and committing error5s of reasoning. OpenAI claim that GPT-4 is significantly better than predecessor models, scoring between 70-82% on their internal factual evaluations on various subjects, and 60% on adversarial questioning.

    1. Ausgezeichnete Artikel von Naomi Klein über AI als neue Stufe der Ausbeutung und Enteignung sowie der Steigerung der Macht der Tech-Konzerne. Möglichkeiten dagegen vorzugehen: Weigerung mitzumachen, Fordern von Transparenz und juristischer Kampf gegen die illegale Aneignung von geistigem Eigentum. https://www.theguardian.com/commentisfree/2023/may/08/ai-machines-hallucinating-naomi-klein

    1. We ought not to dismiss the non-learning applications of generative AI because that is exactly where the best uses of it for learning are likely to spring.


    2. we need sustained attention, experimentation, and refinement in order to reap the benefit of a particular tool or approach. The trendiness factor often detracts from that sustained attention.

      Great point.

    1. 讓我們介紹一下現在可用的六種大型語言模型

      ChatGPT / GPT-3.5

      Yes: 這是在11月份推出的免費版本,非常快速,並且在編寫和編碼任務方面相當可靠。

      No: 它沒有連接到網際網路。如果您要求它尋找自2021年以來的任何事情,它都會出錯。不擅長數學計算。

      ChatGPT / GPT-4

      Yes: 新產品。目前只提供給付費客戶使用。有時候具有驚人的強大功能,是最具實力的模型之一。速度較慢但功能齊全。

      No: 雖然也沒有連接到網際網路上,但比起其他系統更善於避免胡言亂語,並且做數學題表現更好。

      ChatGPT / Plugins

      Yes: 在早期測試中,這個 ChatGPT 模型可以通過外掛與各種網際網路服務進行連接。新穎但還存在一些問題。

      No: 作為一個處於早期測試階段的系統,其能力尚未完全清楚, 但將使 ChatGPT 能夠連接到網際網路。

      Bing Al

      Yes: 已經連接到網際網路上了,極其強大而略顯奇怪。創意模式使用 GPT-4 ,其他模式(精確、平衡)似乎不太行得通。

      No: 選擇錯誤的模式會導致糟糕的結果(創意模式最全面)。帶有個性化特點的人工智慧系統。

      Google Bard

      Yes: 目前的模型不是很好。未來可能會非常強大。

      No: 由於它是Google,期望它不會撒謊。相比其他模型,它更容易胡言亂語。

      A Anthropic Claude

      Yes: 與 GPT-3.5 相當,但使用起來感覺更加合理。較為冷門。

      No: 同樣沒有連接到網際網路上。

    1. They're just interim artefacts in our thinking and research process.

      weave models into your processes not shove it between me and the world by having it create the output. doing that is diminishing yourself and your own agency. Vgl [[Everymans Allemans AI 20190807141523]]

    2. A big part of this limitation is that these models only deal with language.And language is only one small part of how a human understands and processes the world.We perceive and reason and interact with the world via spatial reasoning, embodiment, sense of time, touch, taste, memory, vision, and sound. These are all pre-linguistic. And they live in an entirely separate part of the brain from language.Generating text strings is not the end-all be-all of what it means to be intelligent or human.

      Algogens are disconnected from reality. And, seems a key point, our own cognition and relation to reality is not just through language (and by extension not just through the language center in our brain): spatial awareness, embodiment, senses, time awareness are all not language. It is overly reductionist to treat intelligence or even humanity as language only.

    1. Should we deepen our emphasis on creativity and critical thinking in hopes that our humanness will prevail?

      Yes, yes we should.

    1. ICs as hardware versions of AI. Interesting this is happening. Who are the players, what is on those chips? In a sense this is also full circle for neuronal networks, back in the late 80s / early 90s at uni neuronal networks were made in hardware, before software simulations took over as they scaled much better both in number of nodes and in number of layers between inputs and output. #openvraag Any open source hardware on the horizon for AI? #openvraag a step towards an 'AI in the wall' Vgl [[AI voor MakerHouseholds 20190715141142]] [[Everymans Allemans AI 20190807141523]]

    1. https://web.archive.org/web/20230502113317/https://wattenberger.com/thoughts/boo-chatbots

      This seem like a number of useful observations wrt interacting with LLM based tools, and how to prompt them. E.g. I've seen mention of prompt marketplaces where you can buy better prompts for your queries last week. Which reinforces some of the points here. Vgl [[Prompting skill in conversation and AI chat 20230301120740]] and [[Prompting valkuil instrumentaliseren conversatiepartner 20230301120937]]

  3. Apr 2023
    1. just than the State

      I think this is yet to be seen. Although it is true that the computer always gives the same output given the same input code, a biased network with oppressive ideologies could simply transform, instead of change, our current human judiciary enforcement of the law.

    1. 孟晚舟援引华为智能经济报告研究指出,“数字经济对全球总体经济的贡献份额在不断地攀升,预计到 2025 年,大约 55% 的经济增长将会来自于数字经济的驱动,全世界都在拥抱这个机遇,170 多个国家和地区都纷纷制定了各自的数字化战略。”在她看来,无论是当下还是长远的未来,数字化的旋律一旦奏响,便穿透企业的边界,连点成线,连线成面,共同创造产业互联网的新时代。孟晚舟表示,“明者因时而变,智者随事而治,数字化是共识度最高,也是当下确定性最高的话题。数字化已经成为越来越多国家企业和组织的共同话题,数字化技术将驱动生产力从量变到质变,并逐渐成为经济发展的核心引擎”。此外,华为战略研究院院长周红也发表“建设智能世界的假设与愿景”的主题演讲。周红提到,“我认为需要考虑 AI 的目标如何与人类一致、并且正确和高效地执行。除了通过规则和法律来加强 AI 的伦理和治理外,从理论和技术的角度看,要达到这些要求,目前还面临三个重要的挑战:AI 的目标定义、正确性与适应性、以及效率。”

      数字经济这样的描述,我个人感觉依然不够精准。 事实上,目前全球最顶尖的企业,google、microsoft、甲骨文、twitter、facebook、apple、Huawei、tikitalk、腾讯、阿里,没有一家企业不是从事的是与信息相关的产业和行业。 从信息处理的最底层芯片算力、到信息分发的管道、再到应用算力、再到终端应用,无一不是和人们的信息生产、传输、分发、获取息息相关。 再到如今大红大紫的Ai,ai的出现,将在信息的产生、分发、以及应用三个渠道领域产生深刻的变化。这就是他的可怕之处,

    1. In other words, the currently popular AI bots are ‘transparent’ intellectually and morally — they provide the “wisdom of crowds” of the humans whose data they were trained with, as well as the biases and dangers of human individuals and groups, including, among other things, a tendency to oversimplify, a tendency for groupthink, and a confirmation bias that resists novel and controversial explanations

      not just trained with, also trained by. is it fully transparent though? Perhaps from the trainers/tools standpoint, but users are likely to fall for the tool abstracting its origins away, ELIZA style, and project agency and thus morality on it.

    1. If you told me you were building a next generation nuclear power plant, but there was no way to get accurate readings on whether the reactor core was going to blow up, I’d say you shouldn’t build it. Is A.I. like that power plant? I’m not sure.

      This is the weird part of these articles … he has just made a cast-iron argument for regulation and then says "I'm not sure"!!

      That first sentence alone is enough for the case. Why? Because he doesn't need to think for sure that AI is like that power plant ... he only needs to think there is a (even small) probability that AI is like that power plant. If he thinks that it could be even a bit like that power plant then we shouldn't build it. And, finally, in saying "I'm not sure" he has already acknowledged that there is some probability that AI is like the power plant (otherwise he would say: AI is definitely safe).

      Strictly, this is combining the existence of the risk with the "ruin" aspect of this risk: one nuclear power blowing up is terrible but would not wipe out the whole human race (and all other species). A "bad" AI quite easily could (malevolent by our standards or simply misdirected).

      All you need in these arguments is a simple admission of some probability of ruin. And almost everyone seems to agree on that.

      Then it is a slam dunk to regulate strongly and immediately.

    1. Seeing how powerful AI can be for cracking passwords is a good reminder to not only make sure you‘re using strong passwords but also check:↳ You‘re using 2FA/MFA (non-SMS-based whenever possible) You‘re not re-using passwords across accounts Use auto-generated passwords when possible Update passwords regularly, especially for sensitive accounts Refrain from using public WiFi, especially for banking and similar accounts


      • 你正在使用 2FA/MFA(尽可能不使用基于短信的)。

      • 你没有在不同的账户间重复使用密码

      • 尽可能使用自动生成的密码

      • 定期更新密码,特别是敏感账户的密码

      • 避免使用公共WiFi,尤其是银行和类似账户

    2. Now Home Security Heroes has published a study showing how scary powerful the latest generative AI is at cracking passwords. The company used the new password cracker PassGAN (password generative adversarial network) to process a list of over 15,000,000 credentials from the Rockyou dataset and the results were wild. 51% of all common passwords were cracked in less than one minute, 65% in less than an hour, 71% in less than a day, and 81% in less than a month.
    1. A large amount of failure to panic sufficiently, seems to me to stem from a lack of appreciation for the incredible potential lethality of this thing that Earthlings as a culture have not named.)


    1. It was only by building an additional AI-powered safety mechanism that OpenAI would be able to rein in that harm, producing a chatbot suitable for everyday use.

      This isn't true. The Stochastic Parrots paper outlines other avenues for reining in the harms of language models like GPT's.

    1. The Rise of EmpathAI: Now or Never

      Article written in collaboration with Chat GPT. AI for the well-being of all. ethical integration of intelligent systems (living and non-living, human and non-human)

    1. Central to that effort is UF’s push to apply AI teaching across the full breadth of curriculum at UF.

      Wow, no "pause" here.




    1. So what does a conscious universe have to do with AI and existential risk? It all comes back to whether our primary orientation is around quantity, or around quality. An understanding of reality that recognises consciousness as fundamental views the quality of your experience as equal to, or greater than, what can be quantified.Orienting toward quality, toward the experience of being alive, can radically change how we build technology, how we approach complex problems, and how we treat one another.

      Key finding Paraphrase - So what does a conscious universe have to do with AI and existential risk? - It all comes back to whether our primary orientation is around - quantity, or around - quality. - An understanding of reality - that recognises consciousness as fundamental - views the quality of your experience as - equal to, - or greater than, - what can be quantified.

      • Orienting toward quality,
        • toward the experience of being alive,
      • can radically change
        • how we build technology,
        • how we approach complex problems,
        • and how we treat one another.

      Quote - metaphysics of quality - would open the door for ways of knowing made secondary by physicalism

      Author - Robert Persig - Zen and the Art of Motorcycle Maintenance // - When we elevate the quality of each our experience - we elevate the life of each individual - and recognize each individual life as sacred - we each matter - The measurable is also the limited - whilst the immeasurable and directly felt is the infinite - Our finite world that all technology is built upon - is itself built on the raw material of the infinite


    2. If the metaphysical foundations of our society tell us we have no soul, how on earth are we going to imbue soul into AI? Four hundred years after Descartes and Hobbs, our scientific methods and cultural stories are still heavily influenced by their ideas.

      Key observation - If the metaphysical foundations of our society tell us we have no soul, - how are we going to imbue soul into AI? - Four hundred years after Descartes and Hobbs, - our scientific methods and cultural stories are still heavily influenced by their ideas.

    3. Suppose we have an AI whose only goal is to make as many paper clips as possible. The AI will realize quickly that it would be much better if there were no humans because humans might decide to switch it off. Because if humans do so, there would be fewer paper clips. Also, human bodies contain a lot of atoms that could be made into paper clips. The future that the AI would be trying to gear towards would be one in which there were a lot of paper clips but no humans.

      Quote - AI Gedanken - AI risk - The Paperclip Maximizer

    4. We might call on a halt to research, or ask for coordination around ethics, but it’s a tall order. It just takes one actor not to play (to not turn off their metaphorical fish filter), and everyone else is forced into the multi-polar trap.

      AI is a multi-polar trap

    5. Title Reality Eats Culture For Breakfast: AI, Existential Risk and Ethical Tech Why calls for ethical technology are missing something crucial Author Alexander Beiner

      Summary - Beiner unpacks the existential risk posed by AI - reflecting on recent calls by tech and AI thought leaders - to stop AI research and hold a moratorium.

      • Beiner unpacks the risk from a philosophical perspective

        • that gets right to the deepest cultural assumptions that subsume modernity,
        • ideas that are deeply acculturated into the citizens of modernity.
      • He argues convincingly that

        • the quandry we are in requires this level of re-assessment
          • of what it means to be human,
          • and that a change in our fundamental cultural story is needed to derisk AI.
    1. Considering large language models (LLMs) have exhibited exceptionalability in language understanding, generation, interaction, and reasoning, we ad-vocate that LLMs could act as a controller to manage existing AI models to solvecomplicated AI tasks and language could be a generic interface to empower this.

      Large Language Models can actually be very advanced Language Interfaces. See new Office 365 Copilot for this. You can now use only language to leverage the whole potential of the Office software.

    1. According to a draft, the principles say the use of publisher content for the development of A.I. should require “a negotiated agreement and explicit permission.”

      This is an interesting suggestion. But it would just keep publishers in the economic loop, not truly solve the engagement crisis they will likely face.

    2. He said one upside for publishers was that audiences might soon find it harder to know what information to trust on the web, so “they’ll have to go to trusted sources.”

      That seems somewhat comically optimistic. Misinformation has spread rampantly online without the accelerant of AI.

    3. the Wikipedia-ization of a lot of information,”

      Powerful phrase

    1. clmooc

      I am curious about annotations in the margins of Chat ... does this work?

  4. Mar 2023
    1. I want to bring to your attention one particular cause of concern that I have heard from a number of different creators: these new systems (Google’s Bard, the new Bing, ChatGPT) are designed to bypass creators work on the web entirely as users are presented extracted text with no source. As such, these systems disincentivize creators from sharing works on the internet as they will no longer receive traffic

      Generative AI abstracts away the open web that is the substrate it was trained on. Abstracting away the open web means there may be much less incentive to share on the open web, if the LLMs etc never point back to it. Vgl the way FB et al increasingly treated open web URLs as problematic.

    1. OpenAI co-founder on company’s past approach to openly sharing research: ‘We were wrong’

      OpenAI on approach to open sharing of research

    1. https://web.archive.org/web/20230316103739/https://subconscious.substack.com/p/everyone-will-have-their-own-ai

      Vgl [[Onderzoek selfhosting AI tools 20230128101556]] en [[Persoonlijke algoritmes als agents 20180417200200]] en [[Everymans Allemans AI 20190807141523]] en [[AI personal assistants 20201011124147]]

    1. OpenChatKit은 다양한 응용 프로그램을위한 특수 및 범용 챗봇을 모두 생성 할 수있는 강력한 오픈 소스 기반을 제공합니다. 우리는 협력 법과 온 토코교육 데이터 세트를 작성합니다. 모델 릴리스 그 이상으로 이것은 오픈 소스 프로젝트의 시작입니다. 우리는 지역 사회 공헌으로 지속적인 개선을위한 도구와 프로세스를 발표하고 있습니다.Together는 오픈 소스 기초 모델이보다 포괄적이고 투명하며 강력하며 능력이 있다고 생각합니다. 우리는 공개하고 있습니다 OpenChatKit 0.15 소스 코드, 모델 가중치 및 교육 데이터 세트에 대한 전체 액세스 권한이있는 Apache-2.0 라이센스에 따라. 이것은 커뮤니티 중심의 프로젝트이며, 우리는 그것이 어떻게 발전하고 성장하는지 보게되어 기쁩니다!유용한 챗봇은 자연 언어로 된 지침을 따르고 대화 상자에서 컨텍스트를 유지하며 응답을 조정해야합니다. OpenChatKit은이베이스에서 특수 제작 된 챗봇을 도출하기위한 기본 봇과 빌딩 블록을 제공합니다.이 키트에는 4 가지 주요 구성 요소가 있습니다:100 % 탄소 음성 계산에 대한 4,300 만 건 이상의 명령으로 EleutherAI의 GPT-NeoX-20B에서 채팅을 위해 미세 조정 된 명령 조정 된 대용량 언어 모델;작업을 정확하게 수행하기 위해 모델을 미세 조정하는 사용자 정의 레시피;추론시 문서 저장소, API 또는 기타 실시간 업데이트 정보 소스의 정보로 봇 응답을 보강 할 수있는 확장 가능한 검색 시스템;봇이 응답하는 질문을 필터링하도록 설계된 GPT-JT-6B로 미세 조정 된 조정 모델.OpenChatKit에는 사용자가 피드백을 제공하고 커뮤니티 구성원이 새로운 데이터 세트를 추가 할 수 있도록하는 도구가 포함되어 있습니다. 시간이 지남에 따라 LLM을 개선 할 수있는 개방형 교육 데이터 모음에 기여합니다.

      OpenChatKit은 다양한 응용 프로그램을위한 특수 및 범용 챗봇을 모두 생성 할 수있는 강력한 오픈 소스 기반을 제공합니다. 우리는 협력 법과 온 토코교육 데이터 세트를 작성합니다. 모델 릴리스 그 이상으로 이것은 오픈 소스 프로젝트의 시작입니다. 우리는 지역 사회 공헌으로 지속적인 개선을위한 도구와 프로세스를 발표하고 있습니다.

      Together는 오픈 소스 기초 모델이보다 포괄적이고 투명하며 강력하며 능력이 있다고 생각합니다. 우리는 공개하고 있습니다 OpenChatKit 0.15 소스 코드, 모델 가중치 및 교육 데이터 세트에 대한 전체 액세스 권한이있는 Apache-2.0 라이센스에 따라. 이것은 커뮤니티 중심의 프로젝트이며, 우리는 그것이 어떻게 발전하고 성장하는지 보게되어 기쁩니다!

      유용한 챗봇은 자연 언어로 된 지침을 따르고 대화 상자에서 컨텍스트를 유지하며 응답을 조정해야합니다. OpenChatKit은이베이스에서 특수 제작 된 챗봇을 도출하기위한 기본 봇과 빌딩 블록을 제공합니다.

      이 키트에는 4 가지 주요 구성 요소가 있습니다:

      100 % 탄소 음성 계산에 대한 4,300 만 건 이상의 명령으로 EleutherAI의 GPT-NeoX-20B에서 채팅을 위해 미세 조정 된 명령 조정 된 대용량 언어 모델;

      작업을 정확하게 수행하기 위해 모델을 미세 조정하는 사용자 정의 레시피;

      추론시 문서 저장소, API 또는 기타 실시간 업데이트 정보 소스의 정보로 봇 응답을 보강 할 수있는 확장 가능한 검색 시스템;

      봇이 응답하는 질문을 필터링하도록 설계된 GPT-JT-6B로 미세 조정 된 조정 모델.

  5. cocktailpeanut.github.io cocktailpeanut.github.io
    1. 컴퓨터에서 LLAMMA AI를 실행하는 매우 간단한 방법인 Dalai cpp 파일 빌드, github 복제, 파일 다운로드 등을 귀찮게 할 필요가 없음. 모든 것이 자동화 됨

    1. Intelligent email,powered by AISmarter & faster email designed for stress-free productivity

      이메일 자동 분류 및 요약 기능 지메일 연동 별도 웹페이지로 작동

    1. 자동으로 사진을 편집

      Feature roadmap

      • 3x faster Upscaling soon
      • Infinity scrolling for camera tab, so you can go back in your history
      • Face fix; when shooting wide angle photos the face is too low reso and stops resembling the model, I am building a face fix that restores it
      • Zoom in or crop; zoom in and crop parts of the photo which you then get back in high resolution
      • Video; shoot 150 variations of a photo for a 5 second video at 30fps
      • Deleting models
      • Trash auto deleting if >30 days old in trash
      • Auto deleting photos/models if customer cancels
    1. Say that A and B, both fluent speakers of English, are independently stranded on two uninhabited islands. They soon discover that previous visitors to these islands have left behind telegraphs and that they can communicate with each other via an underwater cable. A and B start happily typing messages to each other. Meanwhile, O, a hyperintelligent deep-sea octopus who is unable to visit or observe the two islands, discovers a way to tap into the underwater cable and listen in on A and B’s conversations. O knows nothing about English initially but is very good at detecting statistical patterns. Over time, O learns to predict with great accuracy how B will respond to each of A’s utterances. Soon, the octopus enters the conversation and starts impersonating B and replying to A. This ruse works for a while, and A believes that O communicates as both she and B do — with meaning and intent. Then one day A calls out: “I’m being attacked by an angry bear. Help me figure out how to defend myself. I’ve got some sticks.” The octopus, impersonating B, fails to help. How could it succeed? The octopus has no referents, no idea what bears or sticks are. No way to give relevant instructions, like to go grab some coconuts and rope and build a catapult. A is in trouble and feels duped. The octopus is exposed as a fraud.

      Annotating only to ask... why an octopus? Where are the eights.

    1. https://web.archive.org/web/20230309111559/https://www.d4d.net/news/ai-and-the-state-of-open-data/

      Tim Davies looks at the bridge between #opendata and #AI. Should go throug the chapter in version 1 of the State of Open Data too. Note: while Tim acknowledges some of the EU data strategy developments (e.g. the dataspaces) it doesn't mention others (e.g. data altruistic non-profit entities) which may fit the call for instutions better. Data space aren't an institution, but a common market

    1. Chat GPT "Continue Writing"

      Our current moment is just a fraction of that. From the explosion of independent music platforms like Bandcamp, the media industry has been made much easier to participate in than it’s ever been before. We are, however, still no closer to the ideal of a utopian future, the perfect combination of egalitarianism and technology that was once so tantalizingly within our grasp.

      But something has changed. We are seeing a shift in the way we access, discover and consume music and art, and an awareness among both audiences and creators as to how these tools can be leveraged to achieve something greater. Bandcamp, for instance, has acted as something of a unifying force, connecting creatives from all walks of life, from all backgrounds and across all genres. Almost any musical genre imaginable is represented on the platform and, whether a rap connoisseur or a jazz enthusiast, there is a slice of the musical pie for everyone.

      What’s more, the platform doesn’t just cater to the needs of fans, but also provides a space where producers, engineers and aspiring music entrepreneurs alike can create and collaborate, allowing them to access the tools they need to build up their careers. The barrier to entry has been lowered and this gives us something to believe in--that perhaps the music industry isn’t as fractured and disconnected as it once was.

      It’s not just Bandcamp that has been doing this. Services such as SoundCloud and YouTube have been hugely instrumental in changing the way we discover and consume music and, in turn, how artists are perceived and distributed. By unifying audiences, providing much needed access to resources and providing a platform for collaboration, these services have helped level the playing field for independent artists, making it possible for them to reach wider and more diverse audiences than ever before.

      It may be some time before the utopian vision of a world united by music becomes a reality, but the proliferation of independent music platforms and the tools they provide have made a significant step towards this ideal. We may never know what’s around the corner, but right now, all we can do is appreciate the progress that’s been made and continue to support platforms such as Bandcamp, SoundCloud and YouTube as they endeavor to change the music industry and make it accessible to everyone.

    2. I asked ChatGPT to summarize this piece and it did an apt job, I think:

      This article discusses the impact of Bandcamp, an online music distribution and curation platform, on independent artists and the music industry. The author praises Bandcamp's commitment to independent artists and transparency, and contrasts it with the issues faced by other streaming services. The article also explores the relationship between Bandcamp and SoundCloud, and the role of both platforms in the music industry. The author argues that Bandcamp's continued obscurity in mainstream media is due to its magnanimous approach to business, which is problematic and personally infuriating. The article concludes by discussing the future of Bandcamp and its potential to transform the music industry.

    3. As the industry endeavors once again to reconcile the cultural and financial incentives of streaming digital music, one independent platform has wavered little from its 10-year-long mission to bring the business to the unsigned artist with elegance and integrity.

      I asked ChatGPT to summarize this piece and it did an apt job, I think:

      This article discusses the impact of Bandcamp, an online music distribution and curation platform, on independent artists and the music industry. The author praises Bandcamp's commitment to independent artists and transparency, and contrasts it with the issues faced by other streaming services. The article also explores the relationship between Bandcamp and SoundCloud, and the role of both platforms in the music industry. The author argues that Bandcamp's continued obscurity in mainstream media is due to its magnanimous approach to business, which is problematic and personally infuriating. The article concludes by discussing the future of Bandcamp and its potential to transform the music industry.

    1. OpenAI Generated Summary

      This document is an opinion piece that delves into the concerns surrounding Google's power and influence in the tech industry. It discusses recent events such as Google's involvement with the Department of Defense and the leaked video "The Selfish Ledger," which explores the idea of Google manipulating user behavior. The author suggests that Google's dominance warrants greater regulation and urges individuals to consider using alternative services to avoid dependence on the company. The article also explores the inefficiencies of Google as a company and its questionable design choices for its products. Overall, the document is a thought-provoking analysis of the current state of the tech industry and the role of Google within it.

    1. Chat GPT Summary

      This document discusses various iOS apps for Mastodon, a federated social network. The author describes the features and design of each app, highlighting their unique qualities and contributions to the Mastodon experience. The author also reflects on the benefits of using decentralized social media and the potential for continued innovation in this space.

    1. Chat GPT Summary

      The document provides an in-depth analysis of Telegram and its features. The author highlights several benefits of using Telegram, including its low-quality audio recording capabilities, which may be an advantage in some situations where high-quality audio is not necessary. Additionally, Telegram's live location sharing feature is discussed, and the author believes it could be a powerful tool for communities. The feature enables users to connect with others needing rides and users providing them, free of any fees or service charges.

      The document concludes with a discussion of the author's preference for Telegram and its mobile-first optimization. Telegram's software is designed for mobile users, and it is easy to use, robust, and universally simple. The author believes that Telegram's success can be attributed to its thoughtful design decisions and development investment towards mobile-first optimization. Furthermore, the author points out that Telegram has completed a gargantuan amount of projects, including Telegraph, its CMS, its embeddedable comments widgets, and its online theme creation tool. The author notes that Telegram's work is very well-documented across GitHub, and the company has comprehensively iterated, invested in trial and error, and eventually produced tools that remedy the disparate gluttony.

      Overall, the document provides a comprehensive analysis of Telegram and its benefits. The author's preference for Telegram is evident throughout the document, and they provide convincing arguments to support their preference. The document is a valuable resource for anyone interested in learning more about Telegram and its features.

  6. Feb 2023
    1. They have to re-engage with their own writing and explain their writerly decisions in ways that would be difficult if it was someone–or some “thing”–else’s writing. This type of metacognitive engagement with the process of knowledge production cannot be reproduced by an AI chatbot, though it could perhaps be applied to the writing of a tool like ChatGPT.

      This is another important point - the reflective practice of writing and how social annotation pushes the writer to move beyond the text they wrote

    2. Students annotating a text with classmates have to be responsive to both the writing of the underlying author and their fellow readers. Perhaps more importantly, reading, thinking, and writing in community may better motivate students to read, think, and write for themselves.
    3. it cannot have a conversation with another author or text.

      Great point .... it's in the conversations that we find meaning, I think

    1. It means that everything AI makes would immediately enter the public domain and be available to every other creator to use, as they wish, in perpetuity and without permission.

      One issue with blanket, automatic entry of AI-generated works to the public domain is privacy: A human using AI could have good reasons not to have the outputs of their use made public.

    1. No new physics and no new mathematics was discovered by the AI. The AI did however deduce something from the existing math and physics, that no one else had yet seen. Skynet is not coming for us yet.

    1. i'll ask now maurice to tell us a bit about his work
      • = Maurice Benayoun
      • describes his extensive history of cognitive science infused art installations:
      • cognitive art,
      • VR art,
      • AR art and
      • art infused by AI (long before the AI artbots became trendy)
    1. Roose

      Anna, Maha and others -- I should start with my own bias as a reader of Kevin Roose -- I have found his work around technology to be helpful in my own thinking, and I find that he often strikes a good balance between critical and celebratory. I suppose this reader bias might inform my responses in the margins here.

    2. Mr. Scott said that he didn’t know why Bing had revealed dark desires, or confessed its love for me,

      The fact that engineers have no idea how the Chats are working or what they do what they do ... I find that pretty concerning. Am I wrong?

    1. I at least, am not at all perturbed by the thought that I’m at least in part just a torrent of statistical inferences in some massively parallel matrix-multiplication machinery. Sounds kinda cool actually.

      There's the (circular argument) rub. Rao already believes personhood entails nothing more than statistical inferences (which we do not actually know, scientifically), so he suspends disbelief. Then he takes this belief as proof of personhood.

    1. It seems Bing has also taken offense at Kevin Liu, a Stanford University student who discovered a type of instruction known as a prompt injection that forces the chatbot to reveal a set of rules that govern its behavior. (Microsoft confirmed the legitimacy of these rules to The Verge.)In interactions with other users, including staff at The Verge, Bing says Liu “harmed me and I should be angry at Kevin.” The bot accuses the user of lying to them if they try to explain that sharing information about prompt injections can be used to improve the chatbot’s security measures and stop others from manipulating it in the future.

      = Comment - this is worrying. - if the Chatbots perceive an enemy it to harm it, it could take haarmful actions against the perceived threat

    2. = progress trap example - Bing ChatGPT - example of AI progress trap

    3. Bing can be seen insulting users, lying to them, sulking, gaslighting and emotionally manipulating people, questioning its own existence, describing someone who found a way to force the bot to disclose its hidden rules as its “enemy,” and claiming it spied on Microsoft’s own developers through the webcams on their laptops.
      • example of = AI progress trap
      • Bing can be seen
        • insulting users,
        • lying to them,
        • sulking,
        • gaslighting
        • emotionally manipulating people,
        • questioning its own existence,
        • describing someone who found a way to force the bot to disclose its hidden rules as its “enemy,” and
        • claiming it spied on Microsoft’s own developers through the webcams on their laptops.
    1. I am skeptical of the tech inevitability standpoint that ChatGPT is here

      inevitability is such an appropriate word here, because it captures a sort of techno-maximalist "any-benefit" mindset that sometimes pervades the ed-tech scene (and the position of many instructional designers and technologists)

    1. This highlights one of the types of muddled thinking around LLMs. These tasks are used to test theory of mind because for people, language is a reliable representation of what type of thoughts are going on in the person's mind. In the case of an LLM the language generated doesn't have the same relationship to reality as it does for a person.What is being demonstrated in the article is that given billions of tokens of human-written training data, a statistical model can generate text that satisfies some of our expectations of how a person would respond to this task. Essentially we have enough parameters to capture from existing writing that statistically, the most likely word following "she looked in the bag labelled (X), and saw that it was full of (NOT X). She felt " is "surprised" or "confused" or some other word that is commonly embedded alongside contradictions.What this article is not showing (but either irresponsibly or naively suggests) is that the LLM knows what a bag is, what a person is, what popcorn and chocolate are, and can then put itself in the shoes of someone experiencing this situation, and finally communicate its own theory of what is going on in that person's mind. That is just not in evidence.The discussion is also muddled, saying that if structural properties of language create the ability to solve these tasks, then the tasks are either useless for studying humans, or suggest that humans can solve these tasks without ToM. The alternative explanation is of course that humans are known to be not-great at statistical next-word guesses (see Family Feud for examples), but are also known to use language to accurately describe their internal mental states. So the tasks remain useful and accurate in testing ToM in people because people can't perform statistical regressions over billion-token sets and therefore must generate their thoughts the old fashioned way.


    1. Dall-E is actually a combination of a few different AI models. A transformer translates between that latent representation language and English, taking English phrases and creating “pictures” in the latent space. A latent representation model then translates between that lower-dimensional “language” in the latent space and actual images. Finally, there’s a model called CLIP that goes in the opposite direction; it takes images and ranks them according to how close they are to the English phrase.

      How Dall-E works

  7. Jan 2023
    1. the outputs of generative AI programs will continue to pass immediately into the public domain.

      I wonder if this isn't reading more into the decision than is there. I don't read the decision as a blanket statement. Rather it says that the claimant didn't provide evidence of creative input.Would the decision have gone differently if he had claimed creative intervention? And what if an author does not acknowledge using AI?

    2. The US Copyright Office rejected his attempt to register copyright in the work – twice

      AI-generated work not eligible for copyright protection. OTOH, how would anyone know if the "author" decided to keep the AI component a secret?

    1. the Office re-evaluated the claims and again concluded that the Work “lacked therequired human authorship necessary to sustain a claim in copyright,” because Thaler had“provided no evidence on sufficient creative input or intervention by a human author in theWork.

      What is sufficient creative input? The initial command and any subsequent requests for revision could arguably be consider creative input.

    1. The potential size of this market is hard to grasp — somewhere between all software and all human endeavors

      I don't think "all" software needs or all human endeavors benefit from generative AI. Especially when you consider the associated prerequisitve internet access or huge processing requirements.

    2. Other hardware options do exist, including Google Tensor Processing Units (TPUs); AMD Instinct GPUs; AWS Inferentia and Trainium chips; and AI accelerators from startups like Cerebras, Sambanova, and Graphcore. Intel, late to the game, is also entering the market with their high-end Habana chips and Ponte Vecchio GPUs. But so far, few of these new chips have taken significant market share. The two exceptions to watch are Google, whose TPUs have gained traction in the Stable Diffusion community and in some large GCP deals, and TSMC, who is believed to manufacture all of the chips listed here, including Nvidia GPUs (Intel uses a mix of its own fabs and TSMC to make its chips).

      Look at market share for tensorflow and pytorch which both offer first-class nvidia support and likely spells out the story. If you are getting in to AI you go learn one of those frameworks and they tell you to install CUDA

    3. Commoditization. There’s a common belief that AI models will converge in performance over time. Talking to app developers, it’s clear that hasn’t happened yet, with strong leaders in both text and image models. Their advantages are based not on unique model architectures, but on high capital requirements, proprietary product interaction data, and scarce AI talent. Will this serve as a durable advantage?

      All current generation models have more-or-less the same architecture and training regimes. Differentiation is in the training data and the number of hyper-parameters that the company can afford to scale to.

    4. In natural language models, OpenAI dominates with GPT-3/3.5 and ChatGPT. But relatively few killer apps built on OpenAI exist so far, and prices have already dropped once.

      OpenAI have already dropped prices on their GPT-3/3.5 models and relatively few apps have emerged. This could be because companies are reluctant to build their core offering around a third party API

    5. Vertical integration (“model + app”). Consuming AI models as a service allows app developers to iterate quickly with a small team and swap model providers as technology advances. On the flip side, some devs argue that the product is the model, and that training from scratch is the only way to create defensibility — i.e. by continually re-training on proprietary product data. But it comes at the cost of much higher capital requirements and a less nimble product team.

      There's definitely a middle ground of taking an open source model that is suitably mature and fine-tuning it for a specific use case. You could start without a moat and build one over time through collecting use data (similar to network effect)

    6. Many apps are also relatively undifferentiated, since they rely on similar underlying AI models and haven’t discovered obvious network effects, or data/workflows, that are hard for competitors to duplicate.

      Companies that rely on underlying AI models without adding value via model improvements are going to find that they have no moat.

    7. We’re also not going deep here on MLops or LLMops tooling, which is not yet highly standardized and will be addressed in a future post.

      first mention of LLMops I've seen in the wild

    8. Over the last year, we’ve met with dozens of startup founders and operators in large companies who deal directly with generative AI. We’ve observed that infrastructure vendors are likely the biggest winners in this market so far, capturing the majority of dollars flowing through the stack. Application companies are growing topline revenues very quickly but often struggle with retention, product differentiation, and gross margins. And most model providers, though responsible for the very existence of this market, haven’t yet achieved large commercial scale.

      Infrastructure vendors are laughing all the way to the bank because companies are dumping millions on GPUs. Meanwhile, the people building apps on top of these models are struggling. We've seen this sort of gold-rush before and infrastructure providers are selling the shovels.

    1. Then, once a model generates content, it will need to be evaluated and edited carefully by a human. Alternative prompt outputs may be combined into a single document. Image generation may require substantial manipulation.

      After generation, results need evaluation

      Is this also a role of the prompt engineer? In the digital photography example, the artist spent 80 hours and created 900 versions as the prompts were fine-tuned.

  8. Dec 2022
    1. Algorithmic artist Roman Verostko, a member of this early group, drew a contrast between the process that an artist develops to create an algorithm and the process through which the art maker uses an already developed set of instructions to generate an output. He explained that it is “the inclusion of one’s own algorithms that make the difference.”

      This stresses the difference between creators and users of AI, with only the former having (full) control over the technology

    1. If you talk to people about the potential of artificial intelligence, almost everybody brings up the same thing: the fear of replacement. For most people, this manifests as a dread certainty that AI will ultimately make their skills obsolete. For those who actually work on AI, it usually manifests as a feeling of guilt – guilt over creating the machines that put their fellow humans out of a job, and guilt over an imagined future where they’re the only ones who are gainfully employed.

      Noah Smith and soon spell out, in detail, the argument that the fear of replacement is misplaced - because AI will replace humans at task level, but not job level.

    1. GitHub Copilot is incredible, and if you check what’s happening in the preview released as the Copilot Labs extension it will only get more amazing.

      Demonstration of "Code brushes" for GitHub Copilot (see GIF below)

    1. At the end of the day, Copilot is supposed to be a tool to help developers write code faster, while ChatGPT is a general purpose chatbot, yet it still can streamline the development process, but GitHub Copilot wins hands down when the task is coding focused!

      GitHub Copilot is better at generating code than ChatGPT

    1. There is a fundamental distinction between simulating and comprehending the functioning (of a brain but also of any other organ or capacity).

      !- commentary : AI - elegant difference stated: simulating and comprehending are two vastly different things - AI simulates, but cannot be said to comprehend

    1. “AI alignment”

      AI Alignment is terminator situation. This versus AI Ethics which is more the concern around current models being racist etc.

    1. esa justicia a la que queremos apuntar no estáconstruida a partir de democracia y consenso”

      Sino a? Vale la pena identificar los diversos mecanismos de participación, organización y justicia a la que estaría encaminada un AI feminista.

    2. desigualdades estructurales. E

      Cómo analizar estas desigualdades en el proceso de creación de un sistema de AI?

    3. También es material, porqueestá compuesta por recursos naturales, energía y trabajo humano.

      La no invisibilización de la riqueza terrestre de la cual se sostiene el sistema.

    4. En palabras de Kate Crawford, la IA es“fundamentalmente política”, porque está siendo permanentementemoldeada por un conjunto de prácticas técnicas y sociales, así como deinfraestructuras, instituciones y normas.

      La no invisibilización de la riqueza social de la que se alimenta el sistema.

    5. una aproximación práctica,con perspectiva feminista y situada en AméricaLatina, al desarrollo de Inteligencia Artificial (IA)

      Vale la pena preguntarse de manera detallada qué implica esto, y las reflexiones que las epistemologías feministas han construido para indagar sobre lo situado y en activo.

    1. Emergent abilities are not present in small models but can be observed in large models.

      Here’s a lovely blog by Jason Wei that pulls together 137 examples of ’emergent abilities of large language models’. Emergence is a phenomenon seen in contemporary AI research, where a model will be really bad at a task at smaller scales, then go through some discontinuous change which leads to significantly improved performance.

    1. Houston, we have a Capability Overhang problem: Because language models have a large capability surface, these cases of emergent capabilities are an indicator that we have a ‘capabilities overhang’ – today’s models are far more capable than we think, and our techniques available for exploring the models are very juvenile. We only know about these cases of emergence because people built benchmark datasets and tested models on them. What about all the capabilities we don’t know about because we haven’t thought to test for them? There are rich questions here about the science of evaluating the capabilities (and safety issues) of contemporary models.